Transit Corridor Analysis
City of Philadelphia
2022-01-26
Cecil B. Moore Ave
Overall Corridor Analysis
Overview
The Cecil B. Moore Ave transit corridor runs along Cecil B Moore Av , 17th St , 16th St , Broad St , 12th St , and 11th St between 17th St and 8th St. The corridor serves the routes 23, 16, 4, 701, 3, and 2. There are 25 total bus stops along the corridor. The corridor directly served 4885 riders per day in 2019.
Weekday Ridership
The Cecil B. Moore Ave corridor moves 4885 transit passengers per day. The peak hour for transit passenger movement on the corridor is 15. At the peak hour, the corridor serves 492 riders.
Speed and Reliability
Speed and reliability are two of the most important aspects governing how attractive transit is to the customer.
There is, however, an important distinction between transit speed and transit reliability:
- Speed is how fast the vehicle is moving through the corridor.
- Reliability is how consistent those speeds are, throughout a day or another period of time.
Oftentimes, qualitative research on transit finds that riders remember their worst trip much more vividly than their average (or even their best) trips. This is where reliability is key - providing customers with a consistent trip time is just as important as a fast trip time, because when they budget time for future trips, they have to be reasonably sure that the time budgeted will represent the majority of potential travel time outcomes.
Bus Speeds
Reliability
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Travel Times
Corridor-level travel time is simply the amount of time it takes the bus to travel from one end of the corridor to another. In this case, it is show separated by route and by direction and is averaged for each hour.
Service Hours
Service hours are a measure of how much transit is operated on the corridor. It is simply the sum of all of the runtime on the corridor over a period of time. Service hours is a product of how much transit is provide, but it is also a product of the speed of operations on a corridor. Transit productivity is often measured in terms of service hours because it is the most direct input into the cost of running the service.
Sub-Corridor Analysis
17th to Broad Analytics
Subcorridor Daily Analytics
| route_id | daily_ridership | trips | routes_served | service_hours | riders_per_hour | on_off | dwell_observed_mean | dwell_predicted_mean | dwell_hybrid_mean | dwell_per_onoff | onoff_per_trip | onoff_per_tripstop | avg_segment_speed | avg_speed_10_pct | avg_speed_25_pct | avg_speed_75_pct | avg_speed_90_pct |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 3 | 4,805 | 172 | 3 | 9.2 | 523 | 3,032 | 75.1 | 53.3 | 75.1 | 0.3 | 17.6 | 4.4 | 6 | 4.7 | 5.3 | 6.5 | 7.4 |
| Total | 4,805 | 172 | 3 | 9.2 | 523 | 3,032 | 75.1 | 53.3 | 75.1 | 0.2 | 17.6 | 4.4 | 6 | 4.7 | 5.3 | 6.5 | 7.4 |
Weekday Ridership
Speed and Reliability
Speed
Reliability
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End-to-End Travel Time
## NULLBroad to 9th Analytics
Subcorridor Daily Analytics
| route_id | daily_ridership | trips | routes_served | service_hours | riders_per_hour | on_off | dwell_observed_mean | dwell_predicted_mean | dwell_hybrid_mean | dwell_per_onoff | onoff_per_trip | onoff_per_tripstop | avg_segment_speed | avg_speed_10_pct | avg_speed_25_pct | avg_speed_75_pct | avg_speed_90_pct |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 3 | 3,003 | 172 | 3 | 6.3 | 478.4 | 722 | 30.8 | 26.5 | 30.8 | 0.2 | 4.2 | 0.7 | 10.7 | 9 | 9.5 | 11.5 | 13.1 |
| Total | 3,003 | 172 | 3 | 6.3 | 478.4 | 722 | 30.8 | 26.5 | 30.8 | 0.1 | 4.2 | 0.7 | 10.7 | 9 | 9.5 | 11.5 | 13.1 |
Weekday Ridership
Speed and Reliability
Speed
Reliability
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End-to-End Travel Time
## NULLSub-Corridor Comparison
plot_speed_corridor_comparison(comparison_df)Stop Level Analysis
Stop Level Map
label_stops <- function(dat) {
daily <- dat$daily_stop_analytics
hourly <- dat$stop_route_hourly_analytics
paste0( "Stop:", daily[[i]]$stop_name , "<br>",
"Stop ID: ", daily$stop_id, "<br>",
"Daily Boards", daily$total_ons, "<br>",
"Daily Leaves", daily$total_ons, "<br>",
"Maximum Hourly Dwell: ", hourly$avg_dwell_hybrid)
}
# map of stops that on the corridor
leaflet() %>%
setView(lng = mean(full_stop_data$daily_stop_analytics[[1]]$stop_lon, na.rm=TRUE),
lat = mean(full_stop_data$daily_stop_analytics[[1]]$stop_lat, na.rm=TRUE), zoom = 14) %>%
addProviderTiles(providers$Stamen.Toner) %>%
addCircleMarkers(full_stop_data$daily_stop_analytics[[1]],
lat = full_stop_data$daily_stop_analytics[[1]]$stop_lat,
lng = full_stop_data$daily_stop_analytics[[1]]$stop_lon,
radius = log((full_stop_data$daily_stop_analytics[[1]]$total_ons +
(full_stop_data$daily_stop_analytics[[1]]$total_offs)))*2.55,
color = "blue",
popup = popupTable(full_stop_data$daily_stop_analytics[[1]]))# also add chart/table of global averages for context of subcorridorsDaily Analytics by Stop Table
table_2 <- full_stop_data$daily_stop_analytics[[1]] %>% select(-c(avg_speed, stop_lat, stop_lon))
# kable(table_2, booktabs = TRUE, align = 'c',format.args = list(big.mark = ","),digits=1) %>%
# kable_styling(latex_options = "scale_down") %>%
# row_spec(dim(table_1)[1], bold = T) %>% # format last row
# column_spec(1, italic = T) %>% # format first column
# scroll_box(width = "100%", height = "300px")
reactable(table_2,
filterable = TRUE,
resizable = TRUE,
columns = list(
# route_id = colDef(
# sticky = "left",
# headerStyle = list(borderRight = "1px solid #eee")
# ),
# direction_id = colDef(
# sticky = "left",
# headerStyle = list(borderRight = "1px solid #eee")
# ),
# stop_id = colDef(
# sticky = "left",
# headerStyle = list(borderRight = "1px solid #eee")
# ),
# stop_name = colDef(
# sticky = "left",
# # Add a right border style to visually distinguish the sticky column
# style = list(borderRight = "1px solid #eee"),
# headerStyle = list(borderRight = "1px solid #eee")
# ),
#total_trips = colDef(aggregate = "max"),
#avg_headway = colDef(aggregate = "mean", format = colFormat(digits = 1)),
total_ons = colDef(aggregate = "sum", format = colFormat(digits = 0)),
total_offs = colDef(aggregate = "sum", format = colFormat(digits = 0)),
avg_load = colDef(format = colFormat(digits = 0)),
avg_dwell_observed = colDef(format = colFormat(digits = 2)),
avg_dwell_estimated = colDef(format = colFormat(digits = 2)),
avg_dwell_hybrid = colDef(format = colFormat(digits = 2)),
avg_dwell_per_pass_hybrid = colDef(format = colFormat(digits = 2))
)
)Daily Analytics by Stop/Route Table
- User input to add/subtracts routes - i.e. pull from the route level data?
table_3 <- full_stop_data$stop_route_analytics[[1]] %>% select(route_id, direction_id, everything()) %>% select(-c(routes, avg_speed, stop_lat, stop_lon)) %>% group_by(route_id, direction_id) %>% dplyr::arrange(route_id, direction_id, stop_lon)
reactable(
table_3,
#filterable = TRUE,
groupBy = c("route_id", "direction_id"),
pagination = FALSE,
resizable = TRUE,
height = 500,
columns = list(
# route_id = colDef(
# sticky = "left",
# headerStyle = list(borderRight = "1px solid #eee")
# ),
# direction_id = colDef(
# sticky = "left",
# headerStyle = list(borderRight = "1px solid #eee")
# ),
# stop_id = colDef(
# sticky = "left",
# headerStyle = list(borderRight = "1px solid #eee")
# ),
# stop_name = colDef(
# sticky = "left",
# # Add a right border style to visually distinguish the sticky column
# style = list(borderRight = "1px solid #eee"),
# headerStyle = list(borderRight = "1px solid #eee")
# ),
total_trips = colDef(aggregate = "max"),
avg_headway = colDef(aggregate = "mean", format = colFormat(digits = 1)),
total_ons = colDef(aggregate = "sum", format = colFormat(digits = 1)),
total_offs = colDef(aggregate = "sum", format = colFormat(digits = 1)),
avg_load = colDef(aggregate = "mean", format = colFormat(digits = 1)),
avg_dwell_observed = colDef(aggregate = "mean", format = colFormat(digits = 1)),
avg_dwell_estimated = colDef(aggregate = "mean", format = colFormat(digits = 1)),
avg_dwell_hybrid = colDef(aggregate = "mean", format = colFormat(digits = 1)),
avg_dwell_per_pass_hybrid = colDef(aggregate = "mean", format = colFormat(digits = 1))
),
defaultColDef = colDef(minWidth = 150)
)